Robots have long been widely used in manufacturing processes for many applications. Many different types of sensors are used to guide robots but machine vision is increasingly being used to guide robots in their tasks. Typically such machine vision is used in a two-dimensional application wherein the target object need only be located in an x-y plane, using a single camera. For example see U.S. Pat. No. 4,437,114 LaRussa. However many robotic applications require the robot to locate and manipulate the target in three dimensions. In the past this has typically involved using two or more cameras. For example see U.S. Pat. No. 4,146,924 Birk et al.; and U.S. Pat. No. 5,959,425 Bieman et al. In order to reduce hardware costs and space requirements it is preferable to use a single camera. Prior single camera systems however have used laser triangulation which involves expensive specialized sensors, must be rigidly packaged to maintain geometric relationships, require sophisticated inter-tool calibration methods and tend to be susceptible to damage or misalignment when operating in industrial environments.
Target points on the object have also been used to assist in determining the location in space of the target object using single or multiple cameras. See U.S. Pat. No. 4,219,847 Pinkney et al. and U.S. Pat. Nos. 5,696,673; 5,956,417; 6,044,183 and 6,301,763 all of Pryor and U.S. Pat. No. 4,942,539 of McGee et al. Typically these methods involve computing the position of the object relative to a previous position, which requires knowledge of the 3D pose of the object at the starting point. These methods also tend to not provide the accuracy and repeatability required by industrial applications. There is therefore a need for a method for calculating the 3D pose of objects using only standard video camera equipment that is capable of providing the level of accuracy and repeatability required for vision guidance of robots as well as other applications requiring 3D pose information of objects.